Yao Jing, Wei Lai, Hao Peipei, Liu Zhongliu, Wang Peijun
Department of Radiology, Tongji Hospital of Tongji University, Shanghai, 200065, China.
Institute of Medical Imaging Artificial Intelligence, Tongji University School of Medicine, Shanghai, 200065, China.
Discov Oncol. 2024 Oct 10;15(1):545. doi: 10.1007/s12672-024-01437-8.
This study aims to develop a deep learning (DL) model based on whole-slide images (WSIs) to predict the pathological stage of clear cell renal cell carcinoma (ccRCC). The histopathological images of 513 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training set and validation set according to the ratio of 8∶2. The CLAM algorithm was used to establish the DL model, and the stability of the model was evaluated in the external validation set. DL features were extracted from the model to construct a prognostic risk model, which was validated in an external dataset. The results showed that the DL model showed excellent prediction ability with an area under the curve (AUC) of 0.875 and an average accuracy score of 0.809, indicating that the model could reliably distinguish ccRCC patients at different stages from histopathological images. In addition, the prognostic risk model constructed by DL characteristics showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P = 0.003), and AUC values for predicting 1-, 3- and 5-year overall survival rates were 0.68, 0.69 and 0.69, respectively, indicating that the prediction model had high sensitivity and specificity. The results of the validation set are consistent with the above results. Therefore, DL model can accurately predict the pathological stage and prognosis of ccRCC patients, and provide certain reference value for clinical diagnosis.
本研究旨在基于全切片图像（WSIs）开发一种深度学习（DL）模型，以预测透明细胞肾细胞癌（ccRCC）的病理分期。从癌症基因组图谱（TCGA）数据库下载了513例ccRCC患者的组织病理学图像，并按照8∶2的比例随机分为训练集和验证集。使用CLAM算法建立DL模型，并在外部验证集中评估模型的稳定性。从该模型中提取DL特征以构建预后风险模型，并在外部数据集中进行验证。结果显示，DL模型表现出出色的预测能力，曲线下面积（AUC）为0.875，平均准确率得分为0.809，表明该模型能够从组织病理学图像中可靠地区分不同分期的ccRCC患者。此外，由DL特征构建的预后风险模型显示，高危组患者的总生存率显著低于低危组（P = 0.003），预测1年、3年和5年总生存率的AUC值分别为0.68、0.69和0.69，表明该预测模型具有较高的敏感性和特异性。验证集的结果与上述结果一致。因此，DL模型能够准确预测ccRCC患者的病理分期和预后，为临床诊断提供一定的参考价值。
